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DOE OSTI2022

Metastable phase of UTe 2 formed under high pressure above 5 GPa

Uranium ditelluride (UTe 2 ) has attracted recent interest due to its unique superconducting properties, which include the potential for a topological odd-parity superconducting state. Recently, ac-calorimetry measurements under pressure indicate a change in the ground state of UTe 2 from superconducting to antiferromagnetic at 1.4 GPa. In this work, we investigate the effect of pressure on the crystal structure of UTe 2 up to 25 GPa at room temperature using x-ray diffraction. We find that UTe 2 , which at ambient conditions has an orthorhombic (Immm) structure, transforms to a body-centered tetragonal (I4/mmm) structure at 5 GPa in a quasihydrostatic neon (Ne) pressure-transmitting medium. In the absence of a pressure-transmitting medium, this transformation occurs between 5 and 8 GPa. The data were fit with a third-order Birch-Murnaghan equation of state resulting in values of B 0 = 46.0 ± 0.6 GPa , B' = 9.3 ± 0.5 (no pressure medium), and B 0 = 42.5 ± 2.0 GPa , B' = 9.3 (fixed) (neon pressure medium) for the Immm phase. For the I4/mmm phase, B 0 = 78.9 ± 0.5 GPa GPa and B' = 4.2 ± 0.1 (no pressure-transmitting medium), and B 0 = 70.0 ± 1.1 GPa and B' = 4.1 ± 0.2 (neon pressure medium). The high-pressure tetragonal phase is retained after decompression to ambient pressure, with approximately 30% remaining after 2 days. We argue that the observed phase transition into a higher-symmetry structure at P ~ 5 GPa (orthorhombic to tetragonal) is accompanied by an increase in the shortest distance between uranium atoms from 3.6 Å (orthorhombic) to 3.9 Å (tetragonal), which suggests localization of the 5ƒ electrons, albeit with a 10.7% decrease in volume.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Characterizing skyrmion flow phases with principal component analysis

Principal component analysis (PCA) is a powerful method that can identify patterns in large, complex data sets by constructing low-dimensional order parameters from higher-dimensional feature vectors. There are increasing efforts to use space-and-time-dependent PCA to detect transitions in nonequilibrium systems that are difficult to characterize with equilibrium methods. Here, we demonstrate that feature vectors incorporating the position and velocity information of driven skyrmions moving through random disorder permit PCA to resolve different types of disordered skyrmion motion as a function of driving force and the ratio of the Magnus force to the dissipation. Since the Magnus force creates gyroscopic motion and a finite Hall angle, skyrmions can exhibit a greater range of flow phases than what is observed in overdamped driven systems with quenched disorder. We show that in addition to identifying previously known skyrmion flow phases, PCA detects several additional phases, including different types of channel flow, moving fluids, and partially ordered states. Guided by the PCA analysis, we further characterize the disordered flow phases to elucidate the different microscopic dynamics and show that the changes in the PCA-derived order parameters can be connected to features in bulk transport measures, including the transverse and longitudinal velocity-force curves, differential conductivity, topological defect density, and changes in the skyrmion Hall angle as a function of drive. We discuss how asymmetric feature vectors can be used to improve the resolution of the PCA analysis, and how this technique can be extended to find disordered phases in other nonequilibrium systems with time-dependent dynamics.

36 MATERIALS SCIENCE↗

DOE OSTI2020

High-resolution cryo-EM reconstructions in the presence of substantial aberrations

Here, an analysis is performed of how uncorrected antisymmetric aberrations, such as coma and trefoil, affect cryo-EM single-particle reconstruction (SPR) results, and an analytical formula quantifying information loss owing to their presence is inferred that explains why Fourier-shell coefficient-based statistics may report significantly overestimated resolution if these aberrations are not fully corrected. The analysis is validated with reference-based aberration refinement for two cryo-EM SPR data sets acquired with a 200 kV microscope in the presence of coma exceeding 40 µm, and 2.3 and 2.7 Å reconstructions for 144 and 173 kDa particles, respectively, were obtained. The results provide a description of an efficient approach for assessing information loss in cryo-EM SPR data acquired in the presence of higher order aberrations, and address inconsistent guidelines regarding the level of aberrations that is acceptable in cryo-EM SPR experiments.

3D reconstruction and image processing↗

DOE OSTI2024

Convergence in simulating global soil organic carbon by structurally different models after data assimilation

Abstract Current biogeochemical models produce carbon–climate feedback projections with large uncertainties, often attributed to their structural differences when simulating soil organic carbon (SOC) dynamics worldwide. However, choices of model parameter values that quantify the strength and represent properties of different soil carbon cycle processes could also contribute to model simulation uncertainties. Here, we demonstrate the critical role of using common observational data in reducing model uncertainty in estimates of global SOC storage. Two structurally different models featuring distinctive carbon pools, decomposition kinetics, and carbon transfer pathways simulate opposite global SOC distributions with their customary parameter values yet converge to similar results after being informed by the same global SOC database using a data assimilation approach. The converged spatial SOC simulations result from similar simulations in key model components such as carbon transfer efficiency, baseline decomposition rate, and environmental effects on carbon fluxes by these two models after data assimilation. Moreover, data assimilation results suggest equally effective simulations of SOC using models following either first‐order or Michaelis–Menten kinetics at the global scale. Nevertheless, a wider range of data with high‐quality control and assurance are needed to further constrain SOC dynamics simulations and reduce unconstrained parameters. New sets of data, such as microbial genomics‐function relationships, may also suggest novel structures to account for in future model development. Overall, our results highlight the importance of observational data in informing model development and constraining model predictions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Expanding the access of wearable silicone wristbands in community-engaged research through best practices in data analysis and integration

Wearable silicone wristbands are a rapidly growing exposure assessment technology that offer researchers the ability to study previously inaccessible cohorts and have the potential to provide a more comprehensive picture of chemical exposure within diverse communities. However, there are no established best practices for analyzing the data within a study or across multiple studies, thereby limiting impact and access of these data for larger meta-analyses. We utilize data from three studies, from over 600 wristbands worn by participants in New York City and Eugene, Oregon, to present a first-of-its-kind manuscript detailing wristband data properties. We further discuss and provide concrete examples of key areas and considerations in common statistical modeling methods where best practices must be established to enable meta-analyses and integration of data from multiple studies. Finally, we detail important and challenging aspects of machine learning, meta-analysis, and data integration that researchers will face in order to extend beyond the limited scope of individual studies focused on specific populations.

Bramer, Lisa M.↗

DOE OSTI2024

High-pressure phase transition of olivine-type Mg 2 GeO 4 to a metastable forsterite-III type structure and their equations of state

Germanates are often used as structural analogs of planetary silicates. We have explored the high-pressure phase relations in Mg 2 GeO 4 using diamond-anvil cell experiments combined with synchrotron X-ray diffraction and computations based on density functional theory. Upon room temperature compression, forsterite-type Mg 2 GeO 4 remains stable up to 30 GPa. At higher pressures, a phase transition to a forsterite-III type (Cmc2 1 ) structure was observed, which remained stable to the peak pressure of 105 GPa. Using a third-order Birch Murnaghan fit to the experimental data, we obtained V 0 = 305.1(3) Å3, K 0 = 124.6(14) GPa, and $K'_0$ = 3.86 (fixed) for forsterite-type Mg 2 GeO 4 and V 0 = 263.5(15) Å 3 , K 0 = 175(7) GPa, and $K'_0$ = 4.2 (fixed) for the forsterite-III type phase. The forsterite-III type structure was found to be metastable when compared to the stable assemblage of perovskite/post-perovskite + MgO, as observed during laser-heating experiments. Understanding the phase relations and physical properties of metastable phases is crucial for studying the mineralogy of impact sites, understanding metastable wedges in subducting slabs, and interpreting the results of shock compression experiments.

58 GEOSCIENCES↗

DOE OSTI2023

Characterizing and mitigating coherent errors in a trapped ion quantum processor using hidden inverses

Quantum computing testbeds exhibit high-fidelity quantum control over small collections of qubits, enabling performance of precise, repeatable operations followed by measurements. Currently, these noisy intermediate-scale devices can support a sufficient number of sequential operations prior to decoherence such that near term algorithms can be performed with proximate accuracy (like chemical accuracy for quantum chemistry problems). While the results of these algorithms are imperfect, these imperfections can help bootstrap quantum computer testbed development. Demonstrations of these algorithms over the past few years, coupled with the idea that imperfect algorithm performance can be caused by several dominant noise sources in the quantum processor, which can be measured and calibrated during algorithm execution or in post-processing, has led to the use of noise mitigation to improve typical computational results. Conversely, benchmark algorithms coupled with noise mitigation can help diagnose the nature of the noise, whether systematic or purely random. Here, we outline the use of coherent noise mitigation techniques as a characterization tool in trapped-ion testbeds. We perform model-fitting of the noisy data to determine the noise source based on realistic physics focused noise models and demonstrate that systematic noise amplification coupled with error mitigation schemes provides useful data for noise model deduction. Further, in order to connect lower level noise model details with application specific performance of near term algorithms, we experimentally construct the loss landscape of a variational algorithm under various injected noise sources coupled with error mitigation techniques. This type of connection enables application-aware hardware codesign, in which the most important noise sources in specific applications, like quantum chemistry, become foci of improvement in subsequent hardware generations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

Data to Accompany: Expanding the access of wearable silicone wristbands in community-engaged research through best practices in data analysis and integration

Bramer, Lisa M↗

DOE OSTI2023

Uncertainties in ab initio nuclear structure calculations with chiral interactions

We present theoretical ground state energies and their uncertainties for p -shell nuclei obtained from chiral effective field theory internucleon interactions as a function of chiral order, fitted to two- and three-body data only. We apply a Similary Renormalization Group transformation to improve the numerical convergence of the many-body calculations, and discuss both the numerical uncertainties arising from basis truncations and those from omitted induced many-body forces, as well as chiral truncation uncertainties. With complete Next-to-Next-to-Leading (N 2 LO) order two- and three-body interactions, we find significant overbinding for the ground states in the upper p -shell, but using higher-order two-body potentials, in combination with N 2 LO three-body forces, our predictions agree with experiment throughout the p -shell to within our combined estimated uncertainties. The uncertainties due to chiral order truncation are noticeably larger than the numerical uncertainties, but they are expected to become comparable to the numerical uncertainties at complete N 3 LO.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

The Challenges Ahead for Multimessenger Analyses of Gravitational Waves and Kilonova: A Case Study on GW190425

In recent years, there have been significant advances in multimessenger astronomy due to the discovery of the first, and so far only confirmed, gravitational wave event with a simultaneous electromagnetic (EM) counterpart, as well as improvements in numerical simulations, gravitational wave (GW) detectors, and transient astronomy. This has led to the exciting possibility of performing joint analyses of the GW and EM data, providing additional constraints on fundamental properties of the binary progenitor and merger remnant. Here, in this work, we present a new Bayesian framework that allows inference of these properties, while taking into account the systematic modeling uncertainties that arise when mapping from GW binary progenitor properties to photometric light curves. We extend the relative binning method presented in Zackay et al. to include extrinsic GW parameters for fast analysis of the GW signal. The focus of our EM framework is on light curves arising from r-process nucleosynthesis in the ejected material during and after merger, the so-called kilonova, and particularly on black hole–neutron star systems. As a case study, we examine the recent detection of GW190425, where the primary object is consistent with being either a black hole or a neutron star. We show quantitatively how improved mapping between binary progenitor and outflow properties, and/or an increase in EM data quantity and quality are required in order to break degeneracies in the fundamental source parameters.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Phylogenomics and diversification of Sordariomycetes

The Sordariomycetes is a specious, morphologically diverse, and widely distributed class of the phylum Ascomycota that forms a well-supported clade diverged from Leotiomycetes. Aside from their ecological significance as plant and human pathogens, saprobes, endophytes, and fungicolous taxa, species of Sordariomycetes produces a wide range of chemically novel and diverse metabolites used in important fields. Recent phylogenetic analyses derived from a small number of genes have considerably increased our understanding of the family, order, and subclass relationships within Sordariomycetes, but several important groups have not been resolved well. In addition, there are various paraphyletic or polyphyletic groups. Moreover, the criteria used to establish higher ranks remain highly variable across different studies. Therefore, the taxonomy of Sordariomycetes is in constant flux, remains poorly understood, and is subject to much controversy. Here, for the first time, we have assembled a phylogenetic dataset containing 638 genomes representing the 156 genera, 50 families, and 17 orders and 5 subclasses of Sordariomycetes. This data set is based on 1124 genes and results in a well-resolved phylogenomic tree. We further constructed an evolutionary timeline of Sordariomycetes diversification based on the genomic data sets. Our divergence time estimate results are inconsistent with previous studies, suggesting estimates of node ages are less precise and varied. Based on these results, we discuss the higher ranks of Sordariomycetes and empirically propose an unprecedented taxonomic framework for the class.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Analysis of MINERvA tunings of GENIE based on Hadron Multiplicity using Machine Learning

Scientists with the MINERvA neutrino interaction experiment have long studied distributions of hadrons (protons and pions) to improve the models for these reactions. Until now, most work has relied on easy to find, energetic, and well tracked hadrons. But in order to take full advantage of MINERνA data it is necessary to develop an efficient enough tool to recognize hadron tracks with lower energy, more transverse angle and more challenging patterns of activity. In this analysis, a Machine Learning tool based on Convolutional Neural Network and Transfer Learning has been developed to tackle that issue. It is found to be extremely capable of recognizing hadron activities especially when there are few and not very energetic hadrons, the accuracy is 76%. But it is also found that this network is very reluctant to classify too many hadrons. The classification is also affected by the presence of other particles in the event such photons, hadrons below a threshold or neutrons. This network is used to compare hadron multiplicity of data to the MC models and distinguish between different MC tunes. Some MINERνA tunes are explored in this analysis, but none of them described the data very well, especially not for all different hadrons multiplicities at once. Four combinations of those tunes were more successful, but none of them could be recognized as the best one. Nevertheless, this analysis develops an ML network that sets the basis of further exploration of hadron multiplicity as a tool to analyze MC tunes at MINERνA and beyond.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Enzymes and Models. In: XAFS Techniques for Catalysts, Nanomaterials, and Surfaces

Many important redox-active metalloenzymes consist of 3d transition metals in their active sites. They catalyze multi-electron reactions in aqueous solution, under ambient temperature and pressure. To understand the functionality of the catalytic sites of these enzymes, X-ray spectroscopy methods provide important information of the electronic structure of the metal of interest and its local structural information, as well as their changes during the catalysis. Studying model compounds, that mimic enzyme catalytic site structures or their functional mimics, is as critical as studying enzymes themselves, in order to interpret X-ray spectroscopic data. In this chapter, various X-ray spectroscopic methods that are useful for structural and electronic structural studies of enzymes and model compounds are described.

Yachandra, VK↗

DOE OSTI2022

Subsurface stress criticality associated with fluid injection and determined using machine learning

Machine-learning methods and apparatus are disclosed to determine critical state or other parameters related to fluid-driven failure of a terrestrial locale impacted by anthropogenic activities such as hydraulic fracturing, hydrocarbon extraction, wastewater disposal, or geothermal harvesting. Acoustic emission, seismic waves, or other detectable indicators of microscopic processes are sensed. A classifier is trained using time series of microscopic data along with corresponding data of critical state or failure events. In disclosed examples, random forests and artificial neural networks are used, and grid-search or EGO procedures are used for hyperparameter tuning. Once trained, the classifier can be applied to live data from a fluid injection locale in order to assess a frictional state, assess seismic hazard, assess permeability, make predictions regarding a future fluid-driven failure event, or drive engineering solutions for mitigation or remediation. Variations are disclosed.

Johnson, Paul Allan↗

DOE OSTI2025

FedOSAA: Improving Federated Learning with One-Step Anderson Acceleration

Federated learning (FL) is a distributed machine learning approach that enables multiple local clients and a central server to collaboratively train a model while keeping the data on their own devices. First-order methods, particularly those incorporating variance reduction techniques, are the most widely used FL algorithms due to their simple implementation and stable performance. However, these methods tend to be slow and require a large number of communication rounds to reach the global minimizer. We propose FedOSAA, a novel approach that preserves the simplicity of first-order methods while achieving the rapid convergence typically associated with second-order methods. Our approach applies one Anderson acceleration (AA) step following classical local updates based on first-order methods with variance reduction, such as FedSVRG and SCAFFOLD, during local training. This AA step is able to leverage curvature information from the history points and gives a new update that approximates the Newton-GMRES direction, thereby significantly improving the convergence. We establish a local linear convergence rate to the global minimizer of FedOSAA for smooth and strongly convex loss functions. Numerical comparisons show that FedOSAA substantially improves the communication and computation efficiency of the original first-order methods, achieving performance comparable to second-order methods like GIANT.

Feng, Xue [University of California, Davis]↗

DOE OSTI2024

Analysis of Waste Material Feedstocks Using Laser-Induced Breakdown Spectroscopy and Machine Learning

Predicting properties such as heating value, ash fusion temperature, and mineral ash composition from Laser-Induced Breakdown Spectroscopy (LIBS) data can make gasifiers more flexible to different feedstocks. Understanding these feedstock properties in-situ improves feedstock conversion modelling methods that allow for consistent operation, higher carbon conversion, and reduced fouling and erosion rates. The purpose of this study is to demonstrate methods for model creation that take LIBS data as predictor features and estimate higher order material properties as a function of feedstock material properties. Six samples were chosen to represent a mixture of abundant and carbon rich waste materials. LIBS measurements were performed on these samples for elemental wavelengths and intensity values. Laboratory analytical results were obtained for each sample’s heating value, proximate and ultimate analysis, mineral ash composition, ash fusion temperatures, and viscosity temperatures. Thermal conductivity was measured using a HotDisk TPS 2500S. LIBS measurements were processed and used as predictor features for machine learning (ML) models to predict the sample’s material properties. Predictor feature selection algorithms, particularly minimum redundancy maximum relevance (mRMR), reduced the dimensionality of ML models. Many modelling methods such as Gaussian process regression (GPR), regression tree, neural networks (NN), and support vector machines (SVM) were demonstrated to be effective at predicting higher order properties; however, mRMR with GPR stood out as a clear winning combination.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2020

Note on the Green’s function formalism and topological invariants

It has been discovered previously that the topological order parameter could be identified from the topological data of the Green's function, namely the (generalized) TKNN invariant in general dimensions, for both non-interacting and interacting systems. In this note, we show that this phenomenon has a clear geometric derivation. In conclusion, this proposal could be regarded as an alternative proof for the identification of the corresponding topological invariant and the topological order parameter.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2020

Weak Lensing And Cosmology In The Dark Energy Survey

In this PhD Thesis, we present studies which aim at understanding and testing the fundamental assumptions of the $\Lambda$CDM cosmological model. We initially show that weak gravitational lensing can be derived as a consequence of General Relativity, and that it can be applied to astronomical data from the Dark Energy Survey (DES) in order to constrain the amplitude of matter fluctuations in the universe. This constraint can be used as an end-to-end cosmological test when it is compared to inferences from the Cosmic Microwave Background. We further extend the cosmological model to account for non-standard parameters which can point to novel physics, and use DES data to assess their statistical significance. We finally show that the Cold Dark Matter (CDM) paradigm can also be fundamentally tested with observational data using morphological signatures of disk galaxies in cluster environments if CDM is self-interacting.

79 ASTRONOMY AND ASTROPHYSICS↗